Nondestructive Inspection Methods Research Articles

Detection of internal defects in RC structures using GPR and ultrasonic methods

The detection of internal defects in reinforced concrete structures is the most important task when conducting surveys of buildings and structures. The ultrasonic research method for studying the structures and determining the possible presence of defects has been known for a long time and has been successfully used to solve various issues directly at construction sites. However, this method has a number of disadvantages, such as the low speed of work and the lack of visual visualization of research results. At the same time, the new technological GPR method is devoid of such disadvantages and, on the contrary, is characterized by high speed and visibility of research results.This article presents the results of field studies of vertical reinforced concrete structures by comparing ultrasonic and GPR methods. As a result of the research, it has been shown that with using of modern non-destructive GPR inspection methods of structures, such internal defects of concreting as pores, cavities, voids and disintegration can be detected efficiently and with high speed. The research results can be presented visually and allow you to more accurately position the location of defects, as well as build 2D and 3D maps of the location of defects.

Autonomous detection of steel corrosion spatial variability in reinforced concrete using X-ray technology and deep learning-based semantic segmentation

Correctly determining the spatial distribution of steel corrosion within a structural member is critical for estimating the remaining service life of deteriorating reinforced concrete (RC) structures. While X-ray technology serves as a nondestructive inspection method, existing challenges persist, particularly in semi-automated corrosion boundary detection. This paper describes a deep learning-based semantic segmentation framework to autonomously detect X-ray images associated with RC, facilitating the visualization of nonuniform steel corrosion distribution. X-ray images were collected from a comprehensive experiment using RC specimens with various structural details by two accelerated corrosion methods. Four deep learning models were constructed, trained, and compared based on the database containing the original X-ray images and the corresponding pixel-level labels. The results demonstrate that the proposed autonomous detection method can segment uncorroded steel at a very high level of global accuracy without time-consuming work, outperforming traditional methods in terms of both accuracy and efficiency.

Relationship between non-destructive inspection and heavy-weight floor impact noises in a box-type test building

This study investigated the relationship between floor impact noise performance and non-destructive inspection methods for use in construction quality control prior to installing resilient floor-topping materials. The study measured floor impact noise characteristics using heavy-weight impact sources in a box-type test building with a 180-mm thick bare slab. The study employed impact echo and ultrasonic tomography methods to estimate concrete thickness and other mechanical properties. The results showed that the highest impact noise levels were observed at the central area in the frequency bands of 25 Hz to 50 Hz. However, the frequency bands of 63 Hz to 80 Hz showed different results as a peak characteristic at the middle area in the long direction. The study estimated the average slab thickness as 175 mm and 172 mm, respectively, using the impact echo and ultrasonic tomography methods. The standard deviation of the thickness was up to 9.3 mm and 6.6 mm, respectively, depending on the impact position. Furthermore, the study analyzed the correlation between concrete thickness and impact noise performance by frequency bands and discussed the application directions of the mechanical properties to manage noise performances during construction.

A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis

Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application.

AZ91 magnezyum alaşımlarının sürtünme karıstırma nokta kaynağı;eksenel kuvvet ve dönme hızının kaynak kalitesine etkisi ve muayene planlamasına ilişkin bir yaklaşım

In this study, the friction stir spot welding (FSSW) process for the magnesium alloy (AZ91) sheet materials are investigated. Friction stir spot welding (FSSW) is a solid-state welding process and one of the innovative methods to join specifically automotive components. Recently magnesium alloys are promising materials where combination of high strength and low density can be observed to improve vehicle performance and reduce emissions and fuel consumption in structural materials for automotive applications. A finite element model (FEM) established to understand the effect of axial force and rotational speed during the simulation in automotive applications. The FEM investigates the effect of axial force ranging from 1 kN to 8 kN and rotational speeds of 1000 rpm - 4000 rpm. The model analyses the heat flux and the temperature rise that leads defect formation after the process. 4.5 kN, 6 kN and 8 kN axial forces and 3000 rpm-4000 rpm rotational speeds are evaluated as the critical values in terms of defect and crack formation during the process under specified boundary conditions. Finally, several non-destructive inspection methods are suggested to secure structural integrity after the FSSW process to eliminate the harmful effects of surface and volumetric discontinuities.

Automatic Detection of Concrete Surface Defects Using Deep Learning and Laser Ultrasonic Visualization Testing

In recent years, nondestructive testing of civil engineering structures has become increasingly important. Non-destructive inspection methods for civil engineering structures generally use ultrasonic waves. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors. Therefore, this study proposes a laser ultrasonic visualization testing using AI. The proposed method will be applied to a concrete structure with surface defects to confirm the effectiveness of the proposed method.

A Modification of Magnetic Adaptive Testing: Progressive Method for Nondestructive Inspection of Microstructural Changes in Ferromagnetic Constructional Materials

Magnetic adaptive testing (MAT), thanks to its relative simplicity from the point of view of both hardware and software, appears to be very promising for non-destructive analysis of various ferromagnetic constructional materials used in many industrial applications. In order to make the inspection of tested objects faster, even more straightforward and the processing of data easier, we concentrated our effort both to improve the experimental procedure, based on specific way of the measurement of magnetization curves at piece-wise linear (triangular) exciting field with a constant field rate of change (i.e., slope) as well as to create a universal software tool for MAT data analysis. On contrary to the original implementation of MAT, the hysteresis loops are measured with decreasing maximum field values, starting at sample saturation region; therefore, time consuming sample demagnetization can be skipped completely. In addition, an advanced tool for the processing of experimentally obtained magnetization curves is presented. Using this application allows to find proper non-traditional magnetic parameters (e.g., the differential permeability) being the most sensitive to various types of industrial load (e.g., thermal, mechanical and/or neutron irradiation) and, at the same time, sufficiently correlated with other, traditional magnetic as well as non-magnetic parameters used routinely for the assessment of possible structural changes associated with applied, often long-term, load even on a microscopic scale, prior to any damage manifests on a macroscopic, visible level. The software capabilities were demonstrated on the data representing the material, whose response to acting thermal load is being difficult to analyze, since the dependence of observed parameters (differential permeability) upon defined artificial ageing was rather complicated. Nevertheless, the sensitivity of differential permeability to such a load was found being more than 8 times larger than in case of traditional hysteretic parameter, namely the remanent flux density while the correlation between them was high.

Corrosion damage detection in reinforced concrete using Rayleigh wave-based method

Corrosion of reinforcing bars is a dominant cause of degradation of reinforced concrete (RC) structures. Over time, expansive products of corrosion build up tensile stresses within the concrete, leading to cracks on the exterior surface and spalling of concrete cover. To extend the service life of RC structures, a reliable non-destructive inspection method is required to detect and evaluate corrosion-induced internal cracks in concrete before spalling, so that timely repair can be conducted. Ultrasonic Rayleigh waves have been employed to detect near-surface cracks in concrete based on their transmission coefficient in frequency domain or their time-of-flight information. However, due to heterogeneity of concrete materials, the ill-defined Rayleigh wave component and the sensor coupling on the rough surface of concrete have a significant influence on accuracy of the method. In this study, an improved method using Rayleigh waves has been investigated to detect corrosion-induced internal cracks in concrete. In the method, energy spectrum of the Rayleigh wave is analysed using the continuous wavelet transform and correlated to locations of internal cracks. This method is not sensitive to surface roughness of concrete members and does not need accurate identification of the Rayleigh wave components. Performance of the method was examined using corroded RC specimens with different cover depths and aggregate sizes. As a comparison, electrochemical tests were performed to map the corrosion current density. The test results showed very good agreement with the corrosion damage map generated from the proposed Rayleigh wave-based method.

NEW NON-DESTRUCTIVE INSPECTION METHOD WITH GROUND PENETRATING RADAR TO EVALUATE THE SUITABILITY OF THE BACKFILLING GROUTING INJECTION IN TUNNEL BORING MACHINES

This research focuses on the development of a new non-destructive inspection method, based on the interpretation of electromagnetic waves by means of a ground penetration radar (GPR) to evaluate the condition of the backfilling two-component mortar grouting behind the segmental lining in tunnels made by tunnel boring machines (TBM). The data processing by digital models and the analysis of the propagation speed of the along the backfilling grouting layer have enabled the technical and operational feasibility of this novel inspection method. Once the conceptual framework of the research was defined, it was possible to validate the developed method in a full-scale operational environment in the Metro of Quito Line 1 (Ecuador). This validation involved a battery of tests collating the results obtained through the core-drilling extractions in comparison with the ones obtained with GPR. As a result of these calibration tests, it was possible to identify a range of propagation speeds linked to grouting in optimal execution conditions, as well as other ranges associated with potential anomalies. Finally, it was possible to implement this new inspection methodology on a regular basis in the Metro of Quito, where the conventional quality control by core-drillings was replaced almost entirely for the new method with GPR. Keywords: tunnel boring machine (TBM); breaching; mortar; ground penetrating radar; electromagnetic.

Pulse compression of guided wave by airborne ultrasound excitation for improving defect detection accuracy in concrete

Scanning airborne ultrasound source technique has been developed as a safe and simple non-contact non-destructive inspection method. This method enables non-contact excitation of guided waves by using high-intensity airborne ultrasound waves. However, the airborne ultrasound source that can generate high intensity airborne ultrasounds uses resonance drive, so the sound waves are emitted for a long time. As a result, the inspection results become complicated, and the inspection accuracy decreases. To solve this problem, we proposed a pulse compression using chirp signals of guided waves generated by airborne ultrasound. In this paper, we investigated the pulse compression of guided waves generated in mortar samples using an airborne ultrasound source driven by a chirp signal. As a result, it was confirmed that the visualization accuracy of defects was improved by pulse compression.

Simultaneous estimation cementite content and non-contact distance in spheroidal graphite cast iron using electromagnetic sensor

The cementite is often deposited on spheroidal graphite cast iron materials used in industrial machinery during manufacturing. Since the cementite is extremely hard and brittle, the cast iron may be damaged when the stress is impressed to the cast iron containing the cementite. Therefore, it is important to estimate the volume fraction of cementite contained in cast iron. However, at present, destructive inspection is generally performed to estimate the presence and volume fraction of cementite in the cast iron. In this research, an electromagnetic non-destructive inspection method to estimate whether the cementite is contained in spheroidal graphite cast iron is proposed. The surface of the cast iron is rough, so the distance (lift-off : Lo) between the electromagnetic sensor and the cast iron is changed slightly depending on the measurement position. By this method, lift-off (Lo) and the cementite content can be detected with an accuracy of less than about 10%.

Nondestructive inspection and imaging of magnetic hydrogel using the alternating magnetic field infrared thermography

Magnetic hydrogel has great potential application in various fields, such as soft robots, drug delivery and biomedical engineering. We report a testing method, i.e., the infrared thermograph method based on alternating magnetic field excitation for the detection of magnetic hydrogel. We verify the feasibility of concentration evaluation of magnetic nanoparticles in magnetic hydrogels using this method and analyze the relationship between the surface temperature rise rate of the magnetic hydrogel and the nanoparticle concentration. Then, we use this method to inspect and image the distribution of magnetic particles. Besides, we validate that this method is effective for detecting bubble defects in magnetic hydrogel and imaging the three-dimensional distribution of the fibers added in the hydrogel, and metal fibers are mainly concerned. This work provides a clear insight into a nondestructive inspection and imaging method to magnetic hydrogel, and this method can be efficient to detect multiple types of information.

Ultrasonic imaging of damage in plates in spectral ripple frequency domain

The ultrasonic C-scan method is a well-known non-destructive inspection method which typically analyzes ultrasound echoes in time domain. Although demonstrated for a variety of defects and materials, it has certain challenges in correctly assessing complex and distributed defects in multi-layer heterogeneous materials, e.g. barely visible impact damage in composite laminates.This paper proposes a C-scan imaging technique in the spectral ripple frequency domain by exploiting interference phenomena between multiple ultrasonic echoes in the response signal. The spectral ripple frequency domain imaging method processes the full A-scan signal without having to define an evaluation time gate. As such, it is highly suited for inspection of curved or tilted parts, or parts having different thickness sections. The method is demonstrated on several 5.5 mm thick carbon fiber reinforced polymer laminates with barely visible impact damage, revealing its capability to resolve neighboring delaminations and small defect fragments over the entire depth of the sample. The spectral ripple frequency domain imaging method shows good performance on datasets with high noise levels and/or low sampling rates, and is computationally very efficient, allowing real-time defect imaging.

Synthesized Evaluation of Reinforced Concrete Bridge Defects, Their Non-Destructive Inspection and Analysis Methods: A Systematic Review and Bibliometric Analysis of the Past Three Decades

Defects are essential indicators to gauge the structural integrity and safety of reinforced concrete bridges. Non-destructive inspection has been pervasively explored over the last three decades to localize and characterize surface and subsurface anomalies in reinforced concrete bridges. In addition, different fuzzy set theory-based, computer vision and artificial intelligence algorithms were leveraged to analyze the data garnered from non-destructive evaluation techniques. In light of the foregoing, this research paper presents a mixed review method that encompasses both bibliometric and systematic analyses of the state-of-the-art work pertinent to the assessment of reinforced concrete bridge defects using non-destructive techniques (CBD_NDT). In this context, this study reviews the literature of journal articles and book chapters indexed in Scopus and Web of Science databases from 1991 to the end of September 2022. To this end, 505 core peer-reviewed journal articles and book chapters are compiled for evaluation after conducting forward and backward snowballing alongside removing irrelevant papers. This research study then exploits both VOSVIEWER and Bibiometrix R Package for the purpose of network visualization and scientometric mapping of the appended research studies. Thereafter, this paper carries out a multifaceted systematic review analysis of the identified literature covering tackled bridge defects, used non-destructive techniques, data processing methods, public datasets, key findings and future research directions. The present study is expected to assist practitioners and policymakers to conceive and synthesize existing research and development bodies, and future trends in the domain of the assessment of bridge defects using non-destructive techniques. It can also aid in raising awareness of the importance of defect management in bridge maintenance systems.

An SVM-Based Health Classifier for Offline Li-Ion Batteries by Using EIS Technology

This paper presents an offline testing framework and simulation to measure the aging situation of Li-ion batteries within the Battery Management System (BMS) or laddering use for maintenance activities. It presents the use case of Electrochemical Impedance Spectroscopy (EIS) as a non-destructive inspection method to detect battery states. Multiple cycles (charge and discharge) were done to gain EIS results in different conditions like temperature. Results were captured and digitalised through a suitable circuit model and mathematical methods for fitting. The State of Health (SOH) values were calibrated, and data were reshaped as vectors and then used as input for Support Vector Machine (SVM). These data were then used to create a machine learning model and analyse the aging mechanism of lithium-ion batteries. The machine learning model is established, and the decision boundaries are visualised in 2D graphs. The accuracy of these machine learning models can reach 80% in the test cases, and good fitting in lifetime tracking. The framework allows more reliable SOH estimation in electric vehicles and more efficient maintenance or laddering operations.

A numerical simulation method of residual magnetic field signals for evaluating deformation-induced stress concentration in ferromagnetic materials

The metal magnetic memory (MMM) technique is a promising non-destructive inspection method that is sensitive to early damage due to stress concentration in ferromagnetic components. However, quantitative analysis methods for evaluating the stress concentration induced by local plastic deformation have not yet been sufficiently studied due to the lack of a reasonable numerical simulation method. Based on the Jiles-Atherton (J-A) magneto-plastic model, which considers the combined effects of residual stress and strain, the change in the behaviour of residual magnetic field (RMF) signals in an X80 pipeline steel specimen with an indentation is calculated in this paper using finite element (FE) simulations, while systematic experimental research is also carried out. The results show that the model-predicted RMF signals are consistent with the experimental data, which demonstrates the validity of the numerical simulation method. Moreover, the comparison indicates that the RMF FE simulations can provide an effective and reliable way to determine the location and the degree of deformation-induced stress concentration. In addition, the effect of the residual stress and strain on the surface magnetic field and the model is discussed. The results of this study help to improve the accuracy of the MMM technique for evaluating the stress concentration caused by local plastic deformation.

Prediction of Weld Tensile-Shear Strength using ANN Based on the Weld Shape in Aluminum Alloy GMAW

Weld shape and size generally determine the quality of gas metal arc welding. Auto parts manufacturers prescribe the size and shape of the weld because they can indicate the mechanical properties of the weld. It is impossible to evaluate the quality of all welds through destruction inspection. Therefore, research on welding quality inspection using laser vision sensors as a non-destructive inspection method is underway. Although the external profile of the weld can be measured using a laser vision sensor, studies to predict the weld strength are insufficient. In this study, an artificial neural network (ANN) model was developed to predict the welding strength of the lap-fillet weld of an aluminum alloy. Input date for weld size was obtained in two ways. In the first method, a bead profile was acquired using a laser vision sensor, whereas the size of the weld was obtained through the acquired bead profile. In the second method, the size of the weld was obtained directly from cross-section analysis. The output data on the strength of the weld was obtained through a tensile shear test. Two models for predicting the tensile shear strength based on ANN were developed. By predicting the tensile strength of both models, the average error rate was within 10%, but the prediction accuracy using the laser vision sensor was better than that of the cross-sectional method.

Application of Non-destructive Testing (NDT) Techniques on Reinforced Concrete Structure: A Review

Abstract: To properly maintain our public infrastructure, engineers and designers must learn different methods of inspection. An exhaustive review has been carried out for different aspects of non- destructive testing (NDT) adopted for RCC structures. NDT evaluates the remaining operation life of different components of structure. It provides an accurate diagnosis which allows prediction of extended life operation beyond the designed life. Different aspects are considered which includes condition assessment, durability, corrosion, condition ranking and service life of structures. In this review, several non-destructive inspection methods are evaluated, with the aim of identifying those, which are practical for detecting defects at early in the production sequence as possible. The methods used for carrying out non destructive analysis used by different investigators are also discussed. Merits and demerits of each method are also stated. RCC structures considered are reinforced buildings, bridges, ESRs, recently developed NDT techniques which are useful for prediction of performance of structure are also included.

Fundamental research on non-destructive testing of reinforced concrete structures using sub-terahertz reflected waves

Terahertz and sub-terahertz waves are unexplored waves range between infrared waves and microwaves. This range features with unique characteristics of both light straightness and electromagnetic wave transmission. Terahertz and sub-terahertz waves are attractively new diagnose method of inner objects because they are safer compared to normal non-destructive inspection methods involving high energy, such as X-rays. Hereby, a novel non-destructive inspection method for concrete and reinforced concrete structures is proposed using sub-terahertz reflection imaging. In Japan, where earthquakes and other natural disasters frequently occur, this technique is expected to be used to remotely inspect the deterioration of damaged RC structures that are difficult to access to. The results of this study confirm that various types of condition changes in concrete can be found by using this sub-terahertz imaging method. The presence of cracks/voids in concrete and under the finishing layers was successfully detected based on differences in reflectance. Moreover, the presence of metals inside/behind the concrete specimens was able to be identified. It was also confirmed that the decline rate of the mechanical properties of concrete could be evaluated according to the decrease in reflectance due to the existence of fine cracks.

Simultaneous Estimation of Void Depth and Plane Size by Electromagnetic Multilayer Scanning Method for Nondestructive Inspection of Outer Wall Tiles

Simultaneous Estimation of Void Depth and Plane Size by Electromagnetic Multilayer Scanning Method for Nondestructive Inspection of Outer Wall Tiles